There are technologies which extract people's reputation about a certain subject by analyzing a large amount of text information. Such technologies are very useful for supporting people's decision making and for marketing. For example, for persons wondering whether or not to buy a certain product, it is important reference information how other people evaluate the product. Further, knowing reputation about a certain product, enterprises can reflect it to development and promotion of a successive product.
In particular, technologies which analyze change in reputation with time by analyzing the number of appearances of evaluations in the form of a time series have attracted much attention in recent years. A first advantage of analyzing change in reputation with time is that it is possible to preclude evaluations of the past which are too old and ineffective. A second advantage is that knowing a cause of a change in reputation becomes an important hint for decision making.
For example, it is supposed that a serious problem about a subject became clear at a point of time t, and since then, reputation of the subject changed and everyone came to have a negative evaluation of the subject. In such a case, evaluations in days after t, where people know the problem, are more important hints for decision making than that in days before t, where people did not know the problem.
Also in such a case, by identifying a point of time of the reputation change, it is possible to know the serious problem about the subject which influenced an evaluation by individual person, and to use it as an important hint for decision making.
Because of the advantages described above, technologies of extracting change in reputation with time from a set of a large number of documents such as blogs have been studied in a variety of ways.
Non-patent document 1 describes a technology which extracts from documents, using an evaluation-expression extraction technology, expressions used by writers of the documents when exhibiting their own evaluations about a subject, and then sums up the numbers of appearances of the expressions and graphs them in the form of a time-series, and thereby presents a change in reputation with time.
The technology described in non-patent document 1 firstly collects evaluation expressions such as “good” and “bad” which are used by writers when exhibiting their evaluations of a subject, by means of mechanical automatic processing, and registers them in a dictionary in advance. The technology described in non-patent document 1 limits evaluation expressions to adjectives and adjectival verbs, and to extract such evaluation expressions, it uses a method of non-patent document 2.
By extracting expressions in documents which agree with expressions in the dictionary, the technology described in non-patent document 1 collects affirmative evaluation expressions and negative evaluation expressions. The technology described in non-patent document 1 regards a graph of the numbers of appearances of affirmative evaluation expressions and negative evaluation expressions as a graph indicating reputation about a subject at each point of time, and outputs it.
Non-patent document 2 describes a technology, which is used in non-patent document 1, of extracting expressions which appear in an unevenly concentrated manner in affirmative reviews and negative reviews as evaluation expressions. The technology described in non-patent document 2 extracts expressions, such as “bright”, “beautiful”, “terrible” and “bad”, which are often used by writers of reviews when exhibiting their own evaluations.
Further, technologies relating to the present invention are described in patent documents 1-3.
A technology described in patent document 1 is a search server for searching for information used to solve a problem, which stores sub-tree information representing a hierarchical structure about a task including an object word and an action word, and stores the object word and action word and a modifier representing the problem, relating the sub-tree information and the group of words to each other. The search server recognizes an object word, an action word and a modifier from inputted search words. The search server acquires stored sub-tree information on the basis of the recognized object word, action word and modifier.
As the search server described in patent document 1 is configured as described above, the user can search for information for solving a problem by a simple and easy input, and thus can reduce the effort of input.
A technology described in patent document 2 is an emotion evaluation system, which collects text information existing on a network, then classifies the text information on the basis of time information obtained along with the text information, and stores the text information in a storage device. From the text information, the emotion evaluation system extracts a combination of an adjective and an adverb which shows the sensitivity, and a noun relevant to the formers, using a dictionary stored in a storage device. Further, the emotion evaluation system assigns, from among adjectives and adverbs relevant to each noun extracted in an emotion information extraction process, an adjective and an adverb whose appearance rates are high as indexes, and generates an emotion map corresponding to each noun, which indicates transitions of appearance rates of adjectives and adverbs resembling respectively the adjective and the adverb assigned as indexes. The emotion evaluation system performs an emotion information mapping process of storing the generated emotion map in a storage device as an emotion map database. The emotion evaluation system performs the following emotion map search process. First, when a search keyword is inputted, the emotion evaluation system searches for an emotion map resembling the most an emotion map created on the basis of a word identical with the search keyword from the emotion map database stored in the storage device. Secondly, the emotion evaluation system outputs the search result as a predictive result of an evaluation.
As the emotion evaluation system described in patent document 2 is configured as described above, the user can search for a transition of emotion with high accuracy.
A technology described in patent document 3 is a time series information processing apparatus, which detects the user's specifying input of a first time series information as time series information in which a date and time is related to a value. The time series information processing apparatus acquires a second time series information for comparing with the first time series information from a database storing a plurality of kinds of time series information. The time series information processing apparatus compares a trend of change during a predetermined period of time in the values in the first time series information with that in the values in the second time series information, and calculates a degree of resemblance between the trends of change as a correlation value by the use of a predetermined evaluation function. When the correlation value is equal to or larger than a predetermined threshold value, the time series information processing apparatus makes an order to display the first and the second time series information in an overlapping form on one screen.
By having the above-described configuration, the time series information processing apparatus described in patent document 3 can provide a technology which visualizes a plurality of pieces of time series information correlated with each other and thereby supports an analysis.